Titles and abstracts

Yong-Yeol Ahn (Indiana University Bloomington, USA)

Title: What can we learn from analogies between brain and society?

Network is a general framework that can be applied to study many complex systems including society and brain. As development of statistical mechanics, statistics, and sociology have been influenced by benefitted from each other, Computational social science and neuroscience may benefit from cross-fertilization of ideas, models, and tools. This talk shows examples of such cross-fertilizations and discusses the implications of the analogy.

Meeyoung Cha (KAIST, Korea & Facebook, USA)

Title: Prominent features of rumors in social networks

Social psychology literature defines a rumor as a story in general circulation without confirmation or certainty to facts. Rumors arise in the context of ambiguity, when the meaning of a situation is not readily apparent or when people feel an acute need for security. Rumors hence are a powerful, pervasive, and persistent force affecting people and groups. This talk will introduce efforts on identifying rumors using massive data in social media. I will discuss the distinct patterns we observed from rumor diffusions in terms of the following aspects: temporal, structural, and linguistic.

Petter Holme (SKKU, Korea)

Title: Structures in time and topology—which is most important for spreading phenomena?

In this talk I will summarize what is known about how the structure of contact patterns affect spreading dynamics like disease spreading. For most of our analyses we will start from temporal networks of human proximity. We will also explore models of contact dynamics, but also argue that this is dangerous since as long as we do not have a catalogue of the possible temporal network structures. The title is our starting point for the discussion, but does not (as you might have guessed) have a yes or no answer.

Hang-Hyun Jo (POSTECH, Korea)

Title: Social Connectome

Thanks to the rapid development of information and communication technology (ICT), a huge amount of digital information has become available. Using time-resolved communication datasets, we are able to study both topological and dynamical structures of social networks in details: (a) topological structure from individuals and their communities to the societal level and (b) dynamical structure from single events and their clusters in time, called bursts, to seasonal behaviors. Topological and dynamical structures of social networks are closely related and they have been studied in terms of temporal networks. For a more detailed understanding of temporal networks, contexts of events have been considered. The context can be defined as the circumstance in which events occur. In the topological sense, one can consider multiple contextual relationships between a pair of individuals, leading to the overlapping communities (OC). In the dynamical sense, bursts can be contextual. Contextual bursts are the bursts defined only for events with the same context. Recently, correlated bursts have also been studied, enabling to explore the contextual correlated bursts (CCB). Using the notions of OC and CCB, we can provide the insights on the following main questions: (a) What does the social network look like? (b) What drives the evolution of the social network? As for (a), the social network could be better described in term of a continuum of overlapping communities. As for (b), burst contextual activities among people will play an important role in the evolution of the social network. For this framework of social networks, we propose the notion of Social Connectome that explores a possibility to make a map of social interaction at a societal level as precisely as possible, given privacy and other constraints.

[1] H.-H. Jo, R.K. Pan, and K. Kaski, Emergence of Bursts and Communities in Evolving Weighted Networks, PLoS ONE 6, e22687 (2011)

[2] H.-H. Jo, M. Karsai, J. Karikoski, and K. Kaski, Spatiotemporal correlations of handset-based service usages, EPJ Data Science 1, 10 (2012)

[3] H.-H. Jo, R.K. Pan, J.I. Perotti, and K. Kaski, Contextual analysis framework for bursty dynamics, Physical Review E 87, 062131 (2013)

[4] Y. Murase, J. Torok, H.-H. Jo, K. Kaski, and J. Kertesz, Multilayer weighted social network model, Physical Review E 90, 052810 (2014)

[5] H.-H. Jo, J.I. Perotti, K. Kaski, and J. Kertesz, Correlated bursts and the role of memory range, Physical Review E 92, 022814 (2015)

[6] Y. Murase, H.-H. Jo, J. Torok, J. Kertesz, and K. Kaski, Modeling the Role of Relationship Fading and Breakup in Social Network Formation, PLoS ONE 10, e0133005 (2015)

[7] H.-H. Jo, Social Connectome (in preparation)

Márton Karsai (ENS Lyon, France)

Title: Local cascades induced global contagion: How heterogeneous thresholds, exogenous effects, and unconcerned behaviour govern online adoption spreading

Adoption of innovations, products or online services is commonly interpreted as a spreading process driven to large extent by social influence and conditioned by the needs and capacities of individuals. To model this process one usually introduces behavioural threshold mechanisms, which can give rise to the evolution of global cascades if the system satisfies a set of conditions. However, these models do not address temporal aspects of the emerging cascades, which in real systems may evolve through various pathways ranging from slow to rapid patterns. Here we fill this gap through the analysis and modelling of product adoption in the world’s largest voice over internet service, the social network of Skype. We provide empirical evidence about the heterogeneous distribution of fractional behavioural thresholds, which appears to be independent of the degree of adopting egos. We show that the structure of real-world adoption clusters is radically different from previous theoretical expectations, since vulnerable adoptions—induced by a single adopting neighbour—appear to be important only locally, while spontaneous adopters arriving at a constant rate and the involvement of unconcerned individuals govern the global emergence of social spreading.

Kimmo Kaski (Aalto University, Finland)

Title: Social Physics: Analysis and Modeling of in situ Human Sociality

As Information Communication Technology (ICT) has become to the hands of practically every one of us it keeps molding our social interactions in many unprecedented ways, the events of which leave behind digital traces of our individual behavior as ever-growing datasets allowing us to empirically understand the structures and processes of social life better. The study of such data using computational analysis and modeling with Network Theory approach can give us unprecedented insight into human sociality. This is well-demonstrated by our analysis of the dataset of mobile phone communication-logs, confirming the Granovetterian picture for the social network structure, i.e. being modular showing communities with strong internal ties and weaker external ties linking them. More recently the same dataset has allowed us to look at the nature of social interaction in more detail and from a different Dunbarian egocentric perspective, due to it including demographic data in the form of gender and age information of individual service subscribers. With this we have got a deeper insight into the gender and age-related social behavior patterns and dynamics of close human relationships. Our analysis results demonstrate sex differences in the gender-bias of preferred relationships that reflect the way the reproductive investment strategies of both sexes change across the lifespan, in particular women's shifting patterns of investment in reproduction and parental care, i.e. showing "grand-mothering" behavior. These empirical findings inspired us to take the next step in network theory, namely developing models to catch some salient features of social networks and processes of human sociality. One of our first models, based on network sociology mechanisms for making friends, turned out to produce many empirically observed Granovetterian features of social networks, like meso-scale community and macro-scale topology formation. This modeling approach has subsequently been extended to take into account social networks being layered, multiplexing or context based, geography dependent, and having relationships between people changing in time. To summarize we believe that social physics approach to social systems opens up a totally new perspective to gain understanding of human sociality.

János Kertész (CEU, Hungary)

Title: The full life cycle of an online social network (with J. Török and Z. Ruan)

The largest Hungarian online social network service, iWiW, was launched 2002 and stopped functioning 12 years later. 2010, at its peak, it had 4.2 million accounts (the population of Hungary is 10 million and there are another ~4 million Hungarians living abroad). We analyze the rise and fall of iWiW. We show how the complex contagion process took place and lead after a slow incubation period rapidly to prevalence, what the typical mechanisms of spreading were, and how geography and social structures influenced the formation of cascades. In our unique data set we are also able to study the reverse process, which was set in due to more successful competing services. We point out the differences and similarities in the social effects leading to the shrinking of the network as compared to the growth period. Inertia or conservative behavioral patterns, which were major hindrances in the acceleration of spreading, contribute to the slowing down of the decay of the network as well. Differences can be identified in the role of the technological development and network structure.

Pádraig Mac Carron (University of Oxford, UK)

Title: Musical Collaboration Networks

Here we analyse a collaboration network of almost two million musicians from the 1930s to 2012. The data were scraped from an online database of over 2 million albums. Two credited musicians on an album are deemed as having collaborated together. The number of active musicians in the database steadily increases over time. As music is often categorised by its decade, we look at the structure of the collaboration network for the last few decades. In spite of an increase in the number of musicians, the structure of the network does not change much between them. Each musician is also tagged as belonging to different genres. This allows us to compare the collaboration networks for different genres. Here we find that some genres have similar properties, while others such as rock and traditional music differ remarkably.

Yohsuke Murase (RIKEN, Japan)

Title: Multilayer weighted social network model

Recent empirical studies using large-scale ICT datasets have generated entirely new, multidisciplinary approaches in social sciences. One of the most prominent outcomes is the validation of the famous Granovetter hypothesis about the "strength of weak ties" using nation-wide mobile phone call data [1]. This hypothesis states the relation between network topology and the link weight: The society consists of communities, which are strongly wired and these communities are then connected by weak ties, thus playing an important role to hold society together. Another important aspect of the socieal network is overlapping community structure [2]. A person can be in different types of relationships, like kinship, collaboration, friendship, etc. Moreover, people are switching their social contexts and communication channels depending on the occasions, and the social network should strongly dependent on the context. To handle these aspects, it is necessary to represent the social networks as a multilayer or multiplex network, where each layer corresponds to a different type of relationship. The aim of this study is to investigate the possibilities to model the combination of the multilayer structure of the society with the Granovetterian relationship between tie strengths and topology. In order to do so, we start from the simple, single-layer weighted social network model by Kumpula et al. [3] and introduce the multilayer structure in different ways. We find that when merging such WSN models, a sufficient amount of interlayer correlation is needed to maintain the relationship between topology and link weights, while these correlations destroy the enhancement in the community overlap due to multiple layers. To resolve this, we devise a geographic multilayer WSN model, where the indirect interlayer correlations due to the geographic constraints of individuals enhance the overlaps between the communities and, at the same time, the Granovetterian structure is preserved [4].

[1] J.-P. Onnela et al., "Structure and tie strengths in mobile communication networks", Proc. Nat. Acad. Sci (2007)

[2] Y.-Y. Ahn et al., "Link communities reveal multiscale complexity in networks", Nature (2010)

[3] J. M. Kumpula et al., "Emergence of communities in weighted networks", Phys. Rev. Lett. (2007)

[4] Y. Murase et al., "Multilayer weighted social network model", Phys. Rev. E (2014)

Takashi Shimada (University of Tokyo, Japan)

Title: Group formation through indirect reciprocity with personal opinion

An interesting and important aspect of the cooperation in human society is that people tend to be more cooperative to the members of the “same group”. It is natural in some cases to assume that such grouping is given by some external condition. However, in some cases like the grouping in beginning students, we cannot find any strong exogenous origin. Therefore it is important to find a self-organized grouping mechanism via social interaction. To study this problem, we take a standard game theory framework of studying the emergence and keeping the cooperative relation. We show that letting the each agent to have their own personal opinion against to the other players yields two clusters state as a stationary state in which agents cooperate only within the cluster. This illustrate the mechanism of keeping the cooperation can naturally lead the segregation of the society. The stability of such two-cluster states against the invasion of agents with different action rule and so on will be also discussed.

K. Oishi, T. Shimada, and N. Ito, “Group formation through indirect reciprocity” Physical Review E Vol. 87, (2013) 030801 (R)